Validation

In order to validate our results, this notebook compare our results with the flood mapping from the ‘Global Flood Monitoring’(GFM) for the same area and time range.

from importlib.resources import files

import holoviews as hv
import hvplot.xarray  # noqa
import numpy as np
import pystac_client
import xarray as xr
from dask.distributed import Client
from dask_flood_mapper import flood
from odc import stac as odc_stac

# define time range and area
time_range = "2023-10-11/2023-10-25"
bounding_box = [12.3, 54.3, 13.1, 54.6]

client = Client(processes=False, threads_per_worker=2, n_workers=1, memory_limit="12GB")

GFM

# Connect to STAC catalog
eodc_catalog = pystac_client.Client.open("https://stac.eodc.eu/api/v1/")

# Search for available images

search = eodc_catalog.search(collections="GFM", bbox=bounding_box, datetime=time_range)
items_GFM = search.item_collection()

print(f"Found {len(items_GFM)} items")

Found 37 items

Load the data

crs = "EPSG:4326"  # Coordinate Reference System - World Geodetic System 1984 (WGS84)
resolution = 0.00018  # 20 meter in degree

GFM_fd = odc_stac.load(
    items_GFM,
    bbox=bounding_box,
    crs=crs,
    bands=["tuw_likelihood", "tuw_flood_extent"],
    resolution=resolution,
    dtype="uint8",
    chunks={"x": 1000, "y": 1000, "time": -1},
)

Pre-processing

# convert values 255 to NANs
GFM_fd["tuw_flood_extent"] = GFM_fd.tuw_flood_extent.where(
    GFM_fd.tuw_flood_extent != 255
).compute()

# select the band 'tuw_flood_extent'
GFM_fd = GFM_fd["tuw_flood_extent"]
GFM_fd

<xarray.DataArray 'tuw_flood_extent' (time: 28, latitude: 1668, longitude: 4445)> Size: 830MB
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
...
        ...,
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ...,  0.,  0.,  0.]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
Coordinates:
  * latitude     (latitude) float64 13kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
  * longitude    (longitude) float64 36kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    spatial_ref  int32 4B 4326
  * time         (time) datetime64[ns] 224B 2023-10-11T05:33:18 ... 2023-10-2...
Attributes:
    nodata:   255

xarray.DataArray

'tuw_flood_extent'

• time: 28
• latitude: 1668
• longitude: 4445

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
  ...
          ...,
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ...,  0.,  0.,  0.]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)

• Coordinates: (4)
  • latitude
    (latitude)
    float64
    54.6 54.6 54.6 ... 54.3 54.3 54.3

    units :

    degrees_north

    resolution :

    -0.00018

    crs :

    EPSG:4326

    array([54.60003, 54.59985, 54.59967, ..., 54.30033, 54.30015, 54.29997])

  • longitude
    (longitude)
    float64
    12.3 12.3 12.3 ... 13.1 13.1 13.1

    units :

    degrees_east

    resolution :

    0.00018

    crs :

    EPSG:4326

    array([12.30003, 12.30021, 12.30039, ..., 13.09959, 13.09977, 13.09995])

  • spatial_ref
    ()
    int32
    4326

    spatial_ref :

    GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    crs_wkt :

    GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984 ensemble

    grid_mapping_name :

    latitude_longitude

    GeoTransform :

    12.299940000000001205648914 0.000180000000000000011336 0 54.600120000000003983586794 0 -0.000180000000000000011336

    array(4326, dtype=int32)

  • time
    (time)
    datetime64[ns]
    2023-10-11T05:33:18 ... 2023-10-...

    array(['2023-10-11T05:33:18.000000000', '2023-10-11T05:33:43.000000000',
           '2023-10-11T05:34:08.000000000', '2023-10-11T16:44:35.000000000',
           '2023-10-11T16:45:00.000000000', '2023-10-13T05:16:57.000000000',
           '2023-10-13T05:17:22.000000000', '2023-10-13T05:17:47.000000000',
           '2023-10-14T17:09:06.000000000', '2023-10-14T17:09:31.000000000',
           '2023-10-14T17:09:56.000000000', '2023-10-16T05:41:42.000000000',
           '2023-10-16T05:42:07.000000000', '2023-10-16T05:42:32.000000000',
           '2023-10-16T16:52:39.000000000', '2023-10-16T16:53:04.000000000',
           '2023-10-16T16:53:29.000000000', '2023-10-19T17:17:02.000000000',
           '2023-10-19T17:17:27.000000000', '2023-10-19T17:17:52.000000000',
           '2023-10-23T05:33:19.000000000', '2023-10-23T05:33:44.000000000',
           '2023-10-23T05:34:09.000000000', '2023-10-23T16:44:36.000000000',
           '2023-10-23T16:45:01.000000000', '2023-10-25T05:16:57.000000000',
           '2023-10-25T05:17:22.000000000', '2023-10-25T05:17:47.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([54.600030000000004,           54.59985,           54.59967,
                     54.59949,           54.59931,           54.59913,
                     54.59895,           54.59877,           54.59859,
                     54.59841,
           ...
           54.301590000000004, 54.301410000000004, 54.301230000000004,
           54.301050000000004,           54.30087,           54.30069,
                     54.30051,           54.30033,           54.30015,
                     54.29997],
          dtype='float64', name='latitude', length=1668))

  • longitude
    PandasIndex

    PandasIndex(Index([12.300030000000001, 12.300210000000002, 12.300390000000002,
           12.300570000000002,           12.30075, 12.300930000000001,
           12.301110000000001, 12.301290000000002, 12.301470000000002,
           12.301650000000002,
           ...
                     13.09833, 13.098510000000001, 13.098690000000001,
           13.098870000000002, 13.099050000000002, 13.099230000000002,
                     13.09941, 13.099590000000001, 13.099770000000001,
           13.099950000000002],
          dtype='float64', name='longitude', length=4445))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-10-11 05:33:18', '2023-10-11 05:33:43',
                   '2023-10-11 05:34:08', '2023-10-11 16:44:35',
                   '2023-10-11 16:45:00', '2023-10-13 05:16:57',
                   '2023-10-13 05:17:22', '2023-10-13 05:17:47',
                   '2023-10-14 17:09:06', '2023-10-14 17:09:31',
                   '2023-10-14 17:09:56', '2023-10-16 05:41:42',
                   '2023-10-16 05:42:07', '2023-10-16 05:42:32',
                   '2023-10-16 16:52:39', '2023-10-16 16:53:04',
                   '2023-10-16 16:53:29', '2023-10-19 17:17:02',
                   '2023-10-19 17:17:27', '2023-10-19 17:17:52',
                   '2023-10-23 05:33:19', '2023-10-23 05:33:44',
                   '2023-10-23 05:34:09', '2023-10-23 16:44:36',
                   '2023-10-23 16:45:01', '2023-10-25 05:16:57',
                   '2023-10-25 05:17:22', '2023-10-25 05:17:47'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (1)

  nodata :

  255

dask-flood-mapper

# calculate flood decision
fd = flood.decision(bbox=bounding_box, datetime=time_range).compute()
fd

sigma naught datacube processed

harmonic parameter datacube processed

projected local incidence angle processed

<xarray.DataArray 'reproject-cd2f0e51db984733b1c696d947bda00b' (time: 9,
                                                                latitude: 1232,
                                                                longitude: 3283)> Size: 291MB
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [ 0.,  0.,  0., ..., nan, nan, nan],
        ...,
        [nan, nan,  0., ..., nan, nan, nan],
        [nan, nan,  0., ..., nan, nan, nan],
        [nan, nan,  0., ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ...,  0., nan, nan],
        ...,
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ...,  0., nan, nan],
        ...,
...
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [ 0.,  0.,  0., ..., nan, nan, nan],
        ...,
        [nan, nan,  0., ..., nan, nan, nan],
        [nan, nan,  0., ..., nan, nan, nan],
        [nan, nan,  0., ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ...,  0., nan, nan],
        ...,
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]]])
Coordinates:
  * time         (time) datetime64[ns] 72B 2023-10-11T05:33:18 ... 2023-10-25...
  * latitude     (latitude) float64 10kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
  * longitude    (longitude) float64 26kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
    spatial_ref  int64 8B 0

xarray.DataArray

'reproject-cd2f0e51db984733b1c696d947bda00b'

• time: 9
• latitude: 1232
• longitude: 3283

• nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan

  array([[[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [ 0.,  0.,  0., ..., nan, nan, nan],
          ...,
          [nan, nan,  0., ..., nan, nan, nan],
          [nan, nan,  0., ..., nan, nan, nan],
          [nan, nan,  0., ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ...,  0., nan, nan],
          ...,
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ...,  0., nan, nan],
          ...,
  ...
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [ 0.,  0.,  0., ..., nan, nan, nan],
          ...,
          [nan, nan,  0., ..., nan, nan, nan],
          [nan, nan,  0., ..., nan, nan, nan],
          [nan, nan,  0., ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ...,  0., nan, nan],
          ...,
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]]])

• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2023-10-11T05:33:18 ... 2023-10-...

    array(['2023-10-11T05:33:18.000000000', '2023-10-13T05:16:57.000000000',
           '2023-10-16T16:53:04.000000000', '2023-10-16T16:53:29.000000000',
           '2023-10-18T05:25:06.000000000', '2023-10-21T17:01:14.000000000',
           '2023-10-21T17:01:39.000000000', '2023-10-23T05:33:19.000000000',
           '2023-10-25T05:16:57.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float64
    54.6 54.6 54.6 ... 54.3 54.3 54.3

    units :

    degrees_north

    resolution :

    -0.00024378434985427075

    crs :

    EPSG:4326

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    array([54.600015, 54.599771, 54.599528, ..., 54.300404, 54.30016 , 54.299917])

  • longitude
    (longitude)
    float64
    12.3 12.3 12.3 ... 13.1 13.1 13.1

    units :

    degrees_east

    resolution :

    0.00024378434985427075

    crs :

    EPSG:4326

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    array([12.300017, 12.300261, 12.300505, ..., 13.09963 , 13.099874, 13.100118])

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    12.299895587547377 0.0002437843498542707 0.0 54.60013705251117 0.0 -0.0002437843498542731

    array(0)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-10-11 05:33:18', '2023-10-13 05:16:57',
                   '2023-10-16 16:53:04', '2023-10-16 16:53:29',
                   '2023-10-18 05:25:06', '2023-10-21 17:01:14',
                   '2023-10-21 17:01:39', '2023-10-23 05:33:19',
                   '2023-10-25 05:16:57'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 54.60001516033625,  54.59977137598639,  54.59952759163654,
            54.59928380728668, 54.599040022936826,  54.59879623858698,
            54.59855245423712,  54.59830866988727,  54.59806488553741,
           54.597821101187556,
           ...
            54.30211068481433, 54.301866900464475,  54.30162311611462,
           54.301379331764764,  54.30113554741491,  54.30089176306505,
           54.300647978715205,  54.30040419436535,  54.30016041001549,
            54.29991662566564],
          dtype='float64', name='latitude', length=1232))

  • longitude
    PandasIndex

    PandasIndex(Index([12.300017479722305, 12.300261264072159, 12.300505048422014,
           12.300748832771868, 12.300992617121722, 12.301236401471577,
            12.30148018582143, 12.301723970171285,  12.30196775452114,
           12.302211538870994,
           ...
           13.097923656795333, 13.098167441145188, 13.098411225495042,
           13.098655009844896, 13.098898794194751, 13.099142578544605,
           13.099386362894458, 13.099630147244314, 13.099873931594168,
           13.100117715944021],
          dtype='float64', name='longitude', length=3283))

• Attributes: (0)

Preprocessing

Make both plots compatible to be plot together.

data_text = files("dask_flood_mapper.data").joinpath("wcover.tif")
wcover = xr.open_dataarray(data_text, band_as_variable=True).rename(
    {"y": "latitude", "x": "longitude"}
)
wcover

<xarray.DataArray 'band_1' (latitude: 1232, longitude: 3283)> Size: 16MB
[4044656 values with dtype=float32]
Coordinates:
  * longitude    (longitude) float64 26kB 12.3 12.3 12.3 12.3 ... 13.1 13.1 13.1
  * latitude     (latitude) float64 10kB 54.6 54.6 54.6 54.6 ... 54.3 54.3 54.3
    spatial_ref  int64 8B ...
Attributes:
    long_name:  wcover

xarray.DataArray

'band_1'

• latitude: 1232
• longitude: 3283

• ...

  [4044656 values with dtype=float32]

• Coordinates: (3)
  • longitude
    (longitude)
    float64
    12.3 12.3 12.3 ... 13.1 13.1 13.1

    array([12.300017, 12.300261, 12.300505, ..., 13.09963 , 13.099874, 13.100118])

  • latitude
    (latitude)
    float64
    54.6 54.6 54.6 ... 54.3 54.3 54.3

    array([54.600015, 54.599771, 54.599528, ..., 54.300404, 54.30016 , 54.299917])

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    12.299895587547379 0.0002437843498542707 0.0 54.60013705251118 0.0 -0.0002437843498542731

    [1 values with dtype=int64]

• Indexes: (2)
  • longitude
    PandasIndex

    PandasIndex(Index([12.300017479722307,  12.30026126407216, 12.300505048422016,
            12.30074883277187, 12.300992617121723, 12.301236401471579,
           12.301480185821433, 12.301723970171286,  12.30196775452114,
           12.302211538870996,
           ...
           13.097923656795334,  13.09816744114519, 13.098411225495044,
           13.098655009844897, 13.098898794194751, 13.099142578544607,
            13.09938636289446, 13.099630147244314,  13.09987393159417,
           13.100117715944023],
          dtype='float64', name='longitude', length=3283))

  • latitude
    PandasIndex

    PandasIndex(Index([54.600015160336255,   54.5997713759864, 54.599527591636544,
           54.599283807286696,  54.59904002293684, 54.598796238586985,
            54.59855245423713, 54.598308669887274,  54.59806488553742,
            54.59782110118757,
           ...
            54.30211068481433,  54.30186690046448,  54.30162311611463,
            54.30137933176477, 54.301135547414916,  54.30089176306506,
           54.300647978715205, 54.300404194365356,   54.3001604100155,
           54.299916625665645],
          dtype='float64', name='latitude', length=1232))

• Attributes: (1)

  long_name :

  wcover

fd = fd.where(wcover != 80)
fd

<xarray.DataArray 'reproject-cd2f0e51db984733b1c696d947bda00b' (time: 9,
                                                                latitude: 8,
                                                                longitude: 381)> Size: 219kB
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ...,  0.,  0.,  0.]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
...
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]],

       [[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ...,  0.,  0.,  0.],
        [nan, nan, nan, ...,  0.,  0.,  0.]]])
Coordinates:
  * time         (time) datetime64[ns] 72B 2023-10-11T05:33:18 ... 2023-10-25...
  * latitude     (latitude) float64 64B 54.34 54.33 54.32 ... 54.31 54.3 54.3
  * longitude    (longitude) float64 3kB 12.3 12.31 12.31 ... 13.1 13.1 13.1
    spatial_ref  int64 8B 0

xarray.DataArray

'reproject-cd2f0e51db984733b1c696d947bda00b'

• time: 9
• latitude: 8
• longitude: 381

• nan nan nan nan nan nan nan nan ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

  array([[[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ...,  0.,  0.,  0.]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
  ...
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan]],
  
         [[nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          [nan, nan, nan, ..., nan, nan, nan],
          ...,
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ...,  0.,  0.,  0.],
          [nan, nan, nan, ...,  0.,  0.,  0.]]])

• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2023-10-11T05:33:18 ... 2023-10-...

    array(['2023-10-11T05:33:18.000000000', '2023-10-13T05:16:57.000000000',
           '2023-10-16T16:53:04.000000000', '2023-10-16T16:53:29.000000000',
           '2023-10-18T05:25:06.000000000', '2023-10-21T17:01:14.000000000',
           '2023-10-21T17:01:39.000000000', '2023-10-23T05:33:19.000000000',
           '2023-10-25T05:16:57.000000000'], dtype='datetime64[ns]')

  • latitude
    (latitude)
    float64
    54.34 54.33 54.32 ... 54.3 54.3

    units :

    degrees_north

    resolution :

    -0.00024378434985427075

    crs :

    EPSG:4326

    axis :

    Y

    long_name :

    latitude

    standard_name :

    latitude

    array([54.338678, 54.326002, 54.320395, 54.314787, 54.313325, 54.307718,
           54.302111, 54.300648])

  • longitude
    (longitude)
    float64
    12.3 12.31 12.31 ... 13.1 13.1 13.1

    units :

    degrees_east

    resolution :

    0.00024378434985427075

    crs :

    EPSG:4326

    axis :

    X

    long_name :

    longitude

    standard_name :

    longitude

    array([12.301968, 12.310744, 12.313913, ..., 13.097192, 13.098899, 13.09963 ])

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    12.299895587547377 0.0002437843498542707 0.0 54.60013705251117 0.0 -0.0002437843498542731

    array(0)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-10-11 05:33:18', '2023-10-13 05:16:57',
                   '2023-10-16 16:53:04', '2023-10-16 16:53:29',
                   '2023-10-18 05:25:06', '2023-10-21 17:01:14',
                   '2023-10-21 17:01:39', '2023-10-23 05:33:19',
                   '2023-10-25 05:16:57'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ 54.33867833729247,  54.32600155110005,   54.3203945110534,
            54.31478747100675,  54.31332476490763,  54.30771772486098,
            54.30211068481433, 54.300647978715205],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([ 12.30196775452114, 12.310743991115894,    12.313913187664,
           12.317082384212105,  12.32025158076021, 12.325858620806859,
           12.329027817354964,  12.33219701390307, 12.335366210451175,
            12.33853540699928,
           ...
           13.086953361051892, 13.089391204550434, 13.090122557599997,
            13.09085391064956, 13.093291754148103, 13.095729597646645,
           13.096460950696208, 13.097192303745771, 13.098898794194751,
           13.099630147244314],
          dtype='float64', name='longitude', length=381))

• Attributes: (0)

Validation plot

common_times = np.intersect1d(GFM_fd.time.values, fd.time.values)


def synced_plot(t):
    plot1 = GFM_fd.sel(time=t).hvplot.image(
        x="longitude",
        y="latitude",
        title="GFM flood map",
        cmap=["rgba(0, 0, 1, 0.1)", "darkblue"],
    )
    plot2 = fd.sel(time=t).hvplot.image(
        x="longitude",
        y="latitude",
        title="dask-flood-mapper",
        cmap=["rgba(0, 0, 1, 0.1)", "darkblue"],
    )

    return (plot1 + plot2).cols(2)


combined_plot = hv.DynamicMap(synced_plot, kdims="time").redim.values(time=common_times)

combined_plot

WARNING:param.Image00328: Image dimension(s) longitude and latitude are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

WARNING:param.Image00328: Image dimension(s) longitude and latitude are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

WARNING:param.Image00328: Image dimension(s) longitude and latitude are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.